Fiche du document
29 juin 2018
- handle: 10670/1.cihqel
- hal: hal-01933858
- doi: 10.2139/ssrn.3200214
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.2139/ssrn.3200214
http://hal.archives-ouvertes.fr/licences/copyright/
Mots-clés
Hidden Markov Models Data Fusion Targeting Customer Analytics C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General J - Labor and Demographic Economics/J.J2 - Demand and Supply of Labor J - Labor and Demographic Economics/J.J4 - Particular Labor Markets M - Business Administration and Business Economics • Marketing • Accounting • Personnel Economics/M.M3 - Marketing and AdvertisingSujets proches
Labor and laboring classesCiter ce document
Matthias Efing et al., « Using Social Network Activity Data to Identify and Target Job Seekers », HAL-SHS : droit et gestion, ID : 10.2139/ssrn.3200214
Métriques
Partage / Export
Résumé
An important challenge for many firms is to identify the life transitions of its customers, such as job searching, being pregnant, or purchasing a home. Inferring such transitions, which are generally unobserved to the firm, can offer the firm opportunities to be more relevant to its customers. In this paper, we demonstrate how a social network platform can leverage its longitudinal user data to identify which of its users are likely job seekers. Identifying job seekers is at the heart of the business model of professional social network platforms. Our proposed approach builds on the hidden Markov model (HMM) framework to recover the latent state of job search from noisy signals obtained from social network activity data. Specifically, our modeling approach combines cross-sectional survey responses to a job seeking status question with longitudinal user activity data. Thus, in some time periods, and for some users, we observe the “true” job seeking status. We fuse the observed state information into the HMM likelihood, resulting in a partially HMM. We demonstrate that the proposed model can not only predict which users are likely to be job seeking at any point in time, but also what activities on the platform are associated with job search, and how long the users have been job seeking. Furthermore, we find that targeting job seekers based on our proposed approach can lead to a 42% increase in profits of a targeting campaign relative to the approach that was used at the time of the data collection.